2019
Kyritsis, Konstantinos; Diou, Christos; Delopoulos, Anastasios
Modeling wrist micromovements to measure in-meal eating behavior from inertial sensor data Journal Article
In: IEEE journal of biomedical and health informatics, 23 (6), pp. 2325–2334, 2019.
Abstract | Links | BibTeX | Tags: Acceleration, Feature extraction, Informatics, Monitoring, Mouth, Obesity, Wrist
@article{kyritsis2019modeling,
title = {Modeling wrist micromovements to measure in-meal eating behavior from inertial sensor data},
author = {Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos},
doi = {10.1109/JBHI.2019.2892011},
year = {2019},
date = {2019-01-01},
journal = {IEEE journal of biomedical and health informatics},
volume = {23},
number = {6},
pages = {2325--2334},
publisher = {IEEE},
abstract = {Overweight and obesity are both associated with in-meal eating parameters such as eating speed. Recently, the plethora of available wearable devices in the market ignited the interest of both the scientific community and the industry toward unobtrusive solutions for eating behavior monitoring. In this paper, we present an algorithm for automatically detecting the in-meal food intake cycles using the inertial signals (acceleration and orientation velocity) from an off-the-shelf smartwatch. We use five specific wrist micromovements to model the series of actions leading to and following an intake event (i.e., bite). Food intake detection is performed in two steps. In the first step, we process windows of raw sensor streams and estimate their micromovement probability distributions by means of a convolutional neural network. In the second step, we use a long short-term memory network to capture the temporal evolution and classify sequences of windows as food intake cycles. Evaluation is performed using a challenging dataset of 21 meals from 12 subjects. In our experiments, we compare the performance of our algorithm against three state-of-theart approaches, where our approach achieves the highest F1 detection score (0.913 in the leave-one-subject-out experiment). The dataset used in the experiments is available at https://mug.ee.auth.gr/intake-cycle-detection/.},
keywords = {Acceleration, Feature extraction, Informatics, Monitoring, Mouth, Obesity, Wrist},
pubstate = {published},
tppubtype = {article}
}
Overweight and obesity are both associated with in-meal eating parameters such as eating speed. Recently, the plethora of available wearable devices in the market ignited the interest of both the scientific community and the industry toward unobtrusive solutions for eating behavior monitoring. In this paper, we present an algorithm for automatically detecting the in-meal food intake cycles using the inertial signals (acceleration and orientation velocity) from an off-the-shelf smartwatch. We use five specific wrist micromovements to model the series of actions leading to and following an intake event (i.e., bite). Food intake detection is performed in two steps. In the first step, we process windows of raw sensor streams and estimate their micromovement probability distributions by means of a convolutional neural network. In the second step, we use a long short-term memory network to capture the temporal evolution and classify sequences of windows as food intake cycles. Evaluation is performed using a challenging dataset of 21 meals from 12 subjects. In our experiments, we compare the performance of our algorithm against three state-of-theart approaches, where our approach achieves the highest F1 detection score (0.913 in the leave-one-subject-out experiment). The dataset used in the experiments is available at https://mug.ee.auth.gr/intake-cycle-detection/.
2017
Kyritsis, Konstantinos; Tatli, Christina Lefkothea; Diou, Christos; Delopoulos, Anastasios
Automated analysis of in meal eating behavior using a commercial wristband IMU sensor Inproceedings
In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2843–2846, IEEE 2017.
Abstract | Links | BibTeX | Tags: Acceleration, Accelerometers, Feature extraction, Gyroscopes, Hidden Markov models, Mouth, Support vector machines
@inproceedings{kyritsis2017automated,
title = {Automated analysis of in meal eating behavior using a commercial wristband IMU sensor},
author = {Konstantinos Kyritsis and Christina Lefkothea Tatli and Christos Diou and Anastasios Delopoulos},
doi = {10.1109/EMBC.2017.8037449},
year = {2017},
date = {2017-01-01},
booktitle = {2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
pages = {2843--2846},
organization = {IEEE},
abstract = {Automatic objective monitoring of eating behavior using inertial sensors is a research problem that has received a lot of attention recently, mainly due to the mass availability of IMUs and the evidence on the importance of quantifying and monitoring eating patterns. In this paper we propose a method for detecting food intake cycles during the course of a meal using a commercially available wristband. We first model micro-movements that are part of the intake cycle and then use HMMs to model the sequences of micro-movements leading to mouthfuls. Evaluation is carried out on an annotated dataset of 8 subjects where the proposed method achieves 0:78 precision and 0:77 recall. The evaluation dataset is publicly available at http://mug.ee.auth.gr/intake-cycle-detection/.},
keywords = {Acceleration, Accelerometers, Feature extraction, Gyroscopes, Hidden Markov models, Mouth, Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}
Automatic objective monitoring of eating behavior using inertial sensors is a research problem that has received a lot of attention recently, mainly due to the mass availability of IMUs and the evidence on the importance of quantifying and monitoring eating patterns. In this paper we propose a method for detecting food intake cycles during the course of a meal using a commercially available wristband. We first model micro-movements that are part of the intake cycle and then use HMMs to model the sequences of micro-movements leading to mouthfuls. Evaluation is carried out on an annotated dataset of 8 subjects where the proposed method achieves 0:78 precision and 0:77 recall. The evaluation dataset is publicly available at http://mug.ee.auth.gr/intake-cycle-detection/.